Chemical looping (CL) is a recently developed process which can be utilized in electrical power generation plants which burn fuels such as coal, biomass, and other opportunity fuels. The CL process can be implemented in power plants, and provides promising improvements in terms of reduced plant size, reduced emissions, and increased plant operational efficiency, among other benefits.
A typical CL system utilizes a high temperature process, whereby solids such as calcium- or metal-based compounds, for example, are “looped” between a first reactor, called an oxidizer, and a second reactor, called a reducer. In the oxidizer, oxygen from air injected into the oxidizer is captured by the solids in an oxidation reaction. The captured oxygen is then carried by the oxidized solids to the reducer to be used for combustion and/or gasification of a fuel such as coal, for example. After a reduction reaction in the reducer, the solids, no longer having the captured oxygen, are returned to the oxidizer to be oxidized again, and the cycle repeats.
Depending on a ratio of the fuel to the air, different gases are produced in the oxidation and reduction reactions. As a result, the ratio of fuel to air can be controlled such that the CL system may be utilized in different ways, such as: as a hybrid combustion-gasification process which produces hydrogen for gas turbines, fuel cells and/or other hydrogen-based applications; as a hybrid combustion-gasification process which produces a synthesis gas (syngas) containing varying amounts of hydrogen and carbon dioxide for gas turbines and/or fuel cells; or as a combustion process for a combustion-based steam power plant.
The CL process is more complicated than processes of traditional plants such as conventional circulating fluidized bed (CFB) plants, for example. As a result, traditional plant controls applied to the CL process necessarily result in separate control loops for each CL loop. However, using separate control loops for each CL loop is inefficient and does not optimize performance of the CL process, since accurate control depends on coordinated control of multiple parameters in each loop, and parameters which crossover between loops.
In addition, the CL process has multi-phase flows and chemical reactions which are characterized by process nonlinearities and time delays due to mass transport and chemical reaction rates. As a result, traditional power plant design without considering control optimization systems in early stages of process design are further inadequate for integrated optimization of process performance and system operability.
Further, many of the variables in the CL process are nonlinear and/or have complex relationships with other variables, e.g., inter-loop interaction of variables. As a result, models which effectively simulate these multi-interdependent variable relationships have thus far been inaccurate, inefficient, and difficult and/or time consuming to work with.
Optimization systems which have been developed thus far are focused on optimizing conventional combustion power plants. Furthermore, these optimization systems have been focused on solving very specific, localized optimization problems rather than global optimization of plant operations. Furthermore, the associated statistical analysis for conventional combustion power plants is based upon an assumption of linear relationships between variables. As a result, the associated statistical analysis for conventional combustion power plants is cumbersome and inaccurate when used to analyze the complex, inter-related, nonlinear dynamics of variables in the CL process.
In the next generation power plants based on a CL system, steam-water side control requirements will remain essentially the same as in current conventional plants (e.g., feedwater and steam flows, steam pressures, steam temperatures, drum levels). However, it is expected that improved controls which utilize both steam-water side variables and combustion/gasification CL variables will be required to better handle inherent process variable interactions in the CL process. In addition, conventional power plant simulators are limited to steam/water side process dynamics and only very simple combustion or furnace process dynamics are modeled; dynamic models of complex atmosphere control systems such as in the CL process are not available at this time.
Process and equipment integration and optimization of the CL system is also needed. More specifically, CL integrated processes are currently not controlled at economically optimum operating conditions. This is especially true during load changes and when other plant disturbances occur. Complex relationships between the many variables and processes described above affect performance of the CL process, and further complicate efforts to optimally and efficiently control the CL process.
Accordingly, it is desired to develop an integrated process design and control optimization system and, more specifically, an integrated process design and control optimization system for a CL power plant, which overcomes the shortfalls described above.